To the best of our knowledge, STML is the only unsupervised metric learning method that can consider semantic relations between data in end-to-end training without introducing off-the-shelf techniques.
Compared to the instance-level surrogate classes [1]}, [2]}, [3]}, [4]}, pseudo labels generated and exploited by STML are more appropriate to capture semantic relations between data since they indicate class-equivalence relations.
Also, unlike previous work based on pseudo labeling [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, STML employs no external algorithm and thus allows training to be efficient, end-to-end, and less sensitive to hyper-parameters.
Further, it is naturally applied to semi-supervised metric learning [11]} as well as unsupervised metric learning.
